Remote non-intrusive occupant space monitoring system

ABSTRACT

A system for remote non-intrusive occupant space monitoring. The system may have sensors and other mechanisms for non-intrusively obtaining information by capturing utility and communication signals, images, light, sound, environmental factors, background information, and so on, about a space and its occupants. The obtained information may be locally or remotely analyzed and modeled by a processor. Models of buildings, behavior, and power systems from the processor may be compared with pre-defined models to infer further information about the space and its occupants. Also, behavioral information may be obtained, inferred and/or learned. The models may be updated with the obtained, inferred and learned information.

This application is a continuation of co-pending U.S. patent applicationSer. No. 12/969,453, filed Dec. 15, 2010, and entitled “A REMOTENON-INTRUSIVE OCCUPANT SPACE MONITORING SYSTEM”, which is incorporatedherein by reference.

BACKGROUND

The invention pertains to obtaining information and particularlyobtaining information about a building and its occupants. Moreparticularly, the invention pertains to monitoring the building and theoccupants from the information obtained.

SUMMARY

The invention is a remote non-intrusive occupant space monitoringsystem. The system may have sensors and other mechanisms fornon-intrusively obtaining information by capturing utility andcommunication signals, images, light, sound, environmental factors,background information, and so on, about a space and its occupants. Theobtained information may be locally and/or remotely analyzed and modeledby a processor. Models of buildings, behaviors and a power system fromthe processor may be compared with pre-defined models to infer furtherinformation about the space and its occupants. Also, behavioralinformation may be obtained, inferred and/or learned. The models may beupdated with the obtained, inferred and learned information.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a block diagram of a non-intrusive acquisition collectionsystem;

FIG. 2 is a flow diagram which shows an operation of the non-intrusiveacquisition collection system;

FIG. 3 is a flow diagram of an illustrative example to elucidate aparticular parameter acquisition, analysis and modeling capability;

FIG. 4 is an illustrative block diagram of an example for modeling of anelement so as to infer further information about the element; and

FIGS. 5 and 6 are diagrams of an illustrative example using electricalpower as a basis for non-intrusively obtaining information about anelement.

DESCRIPTION

Use of non-intrusive monitoring appears attractive for attaininginformation on others in a building. A building may be a residence,commercial building, or industrial facility or complex. It may be costlyand difficult to install appropriate devices in homes or other buildingsin order to monitor occupant behavior. Additionally, residents of aspace often do not necessarily want additional technology installed intheir purview.

The present system may use sensing, analysis, modeling, storage, andcommunications outside of a residence or building in order tocontinuously monitor behavior inside of the building without a need forinstrumentation or equipment in the building. The system may measure andanalyze energy use and other factors about the building, inside andoutside of the building, and use models of typical equipment to infer ordevise what is occurring in the building. The system may also createhuman behavior models based upon these sets of data in order to build anoccupant behavior model. Models (i.e., of the utility power system,building and occupant) may use additional data in order to develop andimprove accuracy of the models. Models may be built and trained aroundhuman behavior as well as building and power system behavior. Thepresent system may allow for an accurate representation of equipmentusage and human behavior without instrumentation situated inside of thebuilding.

For large and more complex sites, it is possible to use a combination ofpublished data about a commercial equipment manufacturer, a power systemmodel of the adjacent area (or the area itself if this data isavailable), domain knowledge and additional data as listed herein (i.e.,temperature, solar index, building information such as type,construction, and so on). Also, any data that is externally available isfair game, including security system data (which may include occupancy),weather data; and for more detailed analysis, the data may include floorplan data, schematics, and the like.

Sensing, models and analyses may have varying complexity depending uponthe application. One may think of the complexity in terms of “planes”,where the simplest plane is the residential application and may use, forexample, a simple current sensor to capture current in the mostinexpensive fashion possible, and then map the current signature toknown equipment in a residential setting, build up the equipment list,and then map the likely behaviors through known patterns of equipment(for instance, furnace fans cycle at a predictable rate, given ambienttemperature, and a furnace model can be quickly created given enoughhistorical measurements). So, when the system is installed, it mayinitially go through a period of training to train the furnace model fora particular home based upon ambient temperature and actual cyclingdetected. Given a few days of measurements, a rudimentary model may beused to determine when the person gets up, leaves and returns home,assuming the person changes the temperature setpoints at home. Overtime, this model may be improved as more data are collected the longerthe system is installed. Once equipment is identified, models of thehuman behavior can be built up. Some of the models may already exist andneed to be tweaked. The models may be built up over time. Models mayupdate themselves with received data via a learning process.

Equipment energy use models might be used to begin with in order toidentify what equipment is running, which may identified through the useof pattern matching algorithms that look for a shape with statisticallyflexible parameters, such as wavelength, amplitude frequency, harmonics,and so on. For instance, a visual query language (VQL) algorithm(available from Honeywell International Inc.) may be used for thispattern matching. An existing model may again be tuned specifically forthe site, once the equipment is identified in order to reduce noise. Inaddition to equipment models, an electric model may be developed usingcommercially available power system modeling software which couldinclude both source and load components. If information is knownregarding the power system components on the site, the components may beincluded (e.g., transformers, switchgear, and so on). If not, thecomponents may be developed through inference based upon the behavior ofthe end site's power system due to system level (substation and higher)disturbances. This model may be refined after data are collected aswell. The equipment model data may be included within the power systemmodel of data in order to build up an accurate representation of theelectrical characteristics of a site. An initial simple model may beginwith black boxes indicating unknown loads (e.g., equipment, plug loads,lights, and so forth) and be refined to reflect their propercharacteristics as more information becomes available including ongoingelectric use data, external data, domain analysis, and so on.

There may be a combination of electric power systems models with othermodels such as predefined predictive, regression, and so forth. Onecomponent may have several types of models. For instance, a transformermay have a behavior model for electrical characteristics and a behaviormodel for mechanical characteristics. There may be various performancemodels for a piece of equipment. The performance models may be used toidentify specific equipment, such as a chiller. The may be several typesof models for a power system such as those including environment versusequipment.

The exact sensor or sensors which may be used in the system might varyupon the application as well. If it is a simple residential application,the quality of the measurements would not necessarily be as high sincethere often is not much equipment and thus it can be relatively easy todifferentiate what is or is not running (so the system can be lessexpensive). Also the need to measure and harmonics is less likely to beneeded. On the other hand, if one is trying to determine what equipmentis running (including a power system configuration) within a largecampus or industrial facility, highly accurate sensor data may berequired along with more complex analysis, including domain analysis.

For complex applications, different sensor sets outside the facilitymight be used such as one or more types of fiber optic current sensors(which may be based upon Faraday effect) and fiber Bragg gratingsinstalled on magnetorestrictive rods. Infrared sensors may be used tomake measurements—at least coarse measurements for current based upontemperature increases in the conductors based upon energy usage.Different types of sensors may be used depending upon the application. Acombination of sensors and techniques including a Rogowski coil formeasuring alternating current (AC) or high speed current pulses may beused. High frequency data may be required in order to measure, store andanalyze harmonics to determine the properties of both the equipment andpower systems equipment within an installation. If the voltage isunavailable for direct measurement, temperatures of the associatedequipment may be measured through various techniques in order todetermine both power usage as well as harmonics (taking ambientconditions into account). For example, the temperatures in the windingsof a transformer will increase greatly when there is a large number ofharmonics in effect. Infrared sensing techniques may be used to infersuch values.

The system may sense current at a high frequency from the electric powerline feeding a building. Other parameters and values may be sensed. Thedata extracted from power line signals may be processed, analyzed andstored. Models may be developed from the data and used in the analysis.The models may be updated over time. Data may be communicated externallyvia convenient communications media which may include cell, PLC, cableor RF to remote places to be used in reports on equipment and humanbehavior at the non-intrusively monitored building of a facility.Generally, the mode of communication is not necessarily critical, as itmay depend upon the application. The present system may be flexible tosupport many physical forms of communication. The system may be modifiedby the present remote system to update models. The remote or centralsite of the system may gather information from virtually all of theinstalled sites, and as the system learns more about equipment behaviorwith respect to power system dynamics, equipment behavior, or peoplebehavior, it may push these learnings back to one or more of theinstalled systems to update the inherent models.

Basically, the system may provide an ability to build a power systemmodel of the bulk power system (utility side) and of the systemconnected (which is a building or group of buildings) and supportstructures. Both of these models may be modified over time, but the bulkside is more stable due to its size. The connected system may be modeledas a single block or can be broken down into as many “load” blocks as isneeded to determine what is going on in the building. These load blocksmay be broken out in several ways. The equipment itself may be a loadand be determined based upon signatures of its electric use. If there istoo much overlapping equipment to do this cleanly, this may be combinedwith the behavior analysis of the loads based upon bulk side systemdisturbances. Additional information may be used including ambientconditions. Once all loads are identified in the building, humanbehavior may start to be identified based upon load characteristics. Ifhuman behavior is the core need for the application, the other data suchas gas, water, and so forth, may be used to make more decisions.

A boundary of a customer's premise may or may not include one or moretransformers. The customer's premise may be a single residential home,commercial building, campus, industrial facility, or micro grid. The oneor more transformers may be dictated by the number of phases of electricpower to be measured. The number of phases required to be measured maydepend upon the installation and where the system is sensing; but formany applications, three phase power may be measured.

For a simple non-intrusive monitoring system, the analysis of dataattained about an element in a building may be automated. For a morecomplex system, a domain expert may be in a loop for analysis, modelingand visualization of data attained about the element in or associatedwith a building. A monitored element may be a person, piece ofequipment, system, system of systems, or any combination of such.

The system may allow for many capabilities which include, but are notlimited to, identifying equipment and/or processes running inside afacility, performing condition-based maintenance, and developingfacility models and human models including those of various behaviors.

There may be an ability of the system to update and create new modelsbased upon learned data, in terms of training and learning purposes, aswell as the creation of new models based upon information after thesystem is installed at a customer site. Examples may incorporate: 1) Anability to update the power system model based upon building and systemreactions to actual physical disturbances on the line; 2) An ability todetermine the best regression model based upon statistical outcome anddata available (choosing of independent parameters for the model); and3) An ability to take user input and modify the evidence used to acalculation or model, or model component used.

An example may elucidate one capability of the present system. A simplecase may have only one monitored variable. In order to increase fidelityof the model, additional sensing components can be added.

An output may include patterns of behavior, which can be in a context oftime of day, day of week, general pattern of movements, and so forth. Abasis for patterns of behavior of a person or persons may include dataabout lights, TV, refrigerator, washer/dryer, and other appliances. Thisdata may be noted, measured and/or inferred from monitoring electricalpower to a residence of the person or persons.

In terms of how to determine what equipment is running based upon sensordata, there are several approaches that may be used: For the most simpleof data, it may be easy to set up a pattern matching algorithm withstatistical bounds to determine when typical equipment are turningon/off such as refrigerator motors, furnace fans, and the like. It mayalso easy to tell what a dishwasher, water heater, washing machine, andso forth, look like without a detailed model to go from.

A power system model may be used as part of the present analysis. Thismodel and other models may be updated through real-time analysis offacilities, buildings and electric power system responses due to powersystem disturbances coming from the electric utility side of the system.

FIG. 1 is a block diagram of an illustrative example of non-intrusiveacquisition collection system. A mechanism 42 may provide non-intrusiveacquisition and collection of information about an element or elements41. Information about element or elements 41 from another basis 43 maybe acquired and collected by mechanism 42. An analysis module 44 may beconnected to mechanism 42. Module 44 may analyze information aboutelement or elements 41 from mechanism 42. Module 44 may utilize varioustechniques and in processing and analysis. Module 44 may request or seekmore information from mechanism 42 via a line 48. The additionalinformation may be updated information or be more detailed. Suchinformation requested or sought may contribute to an interactive processof analyzing by module 44 to provide an output which meets certaincriteria. The criteria may state a level of detail, quality, currentstate, and so forth. An output of module 44 may be information usefulfor modeling at a module 45. More information for modeling may berequested or sought from analysis module 44 via line 49. Also, furtherinformation for modeling by module 45 may be requested from mechanism 42via line 49, module 44 and line 48.

Module 45 may provide models of behavior, status and so on about anelement or elements 41. The models may be a basis for future behavior,status, and so on, about the element or elements 41, developed asresultant information at a development module 46. Module 46 may developother resultant information about the element or elements 41. Theresultant information may go to a user interface 47. Interface 47 mayincorporate a display, a keyboard, and a processor for runningapplications relative to the resultant information, and so on. Userinterface 47 may control acquisition and collection of information frommechanism 42 via a line 51. Interface 47 may control receipt of selectedinformation about the element or elements 41 via line 51. User interface47 may provide and/or control other activity and processes of thepresent system. User interface 47 may also control the system'sinteraction with outside entities.

FIG. 2 is a flow diagram which shows an operation of the present system.One or more elements may be identified for sensing at a symbol 21. Anelement may be a person, a piece of equipment, system, system ofsystems, or any combination of these or other items. As indicated atstep or symbol 22, the element may be observed, measured and/or sensed.Examples of these activities may occur at a sensor module 17 of FIGS. 5and 6. Outputs of the activities at symbol 22 may go to storage asindicated by symbol 23. The outputs may also go for real-time analysisaccording at step or symbol 25. Storage noted at symbol 23 and real-timeanalysis at step or symbol 24 may interact with each other. Outputs fromstorage and analysis as indicated at symbols 23 and 24 may go where theoutputs may be analyzed and models created as indicated by symbol 25.Information about the models from symbol 25 may be fed back for furtherreal-time analysis, updating and improvement at symbol 24. As more islearned about the element and the model is improved, the data that issensed or measured, the analysis and the models themselves may beenhanced or altered, including adding additional data points to improveaccuracy. Additionally, in the case where the element consists ofmultiple elements (as in a system of systems), additional elements to beidentified may occur).

Results from analysis and creation of models of symbol 25 may go forvisualization, allowance for domain input as applicable, and reportingrelative to analysis and models, among other things, at symbol 26. Theoutput of activities noted at symbol 26 may be further analyzed asindicated at symbol 24. A result of the activities at symbol 26 may beinformation regarding the element and the customer's premise asindicated at symbol 27.

The results of visualization and allowance for domain input asapplicable may be fed to symbol 21 where the element is to be identifiedand symbol 22 where the element is to be observed, measured and/orsensed.

The present system may allow for many capabilities which mayincorporate, but are not necessarily limited to, identifying equipmentand/or processes operating inside a facility, performing condition-basedmaintenance, and developing human models including behaviors.

FIG. 3 is a diagram showing an illustrative example to elucidate acapability. The example is a simple case having just one monitoredparameter or variable, such as electrical current. However, to increasefidelity of a model, additional sensing components for monitoring morevariables may be added to the system. An example parameter, such ascurrent, may be non-intrusively observed and measured from outside of,for instance, a home, as indicated by step or symbol 31. Observance andmeasurement data of, for example, current to the home, may be processedand stored as noted at symbol 32. Models at symbol 33 may be developedfrom data as noted in symbol 32. The models, once developed, may beimproved over time upon additional collection of current data andfurther processing.

Processed data may be analyzed as indicated at symbol 34. Results fromdata analysis at symbol 34 may be a basis for creating a human behaviormodel as indicated at symbol 35. Models noted at symbol 33 may beconsidered in behavior model creation. The behavior model may be outputfrom the system as indicated at symbol 36. The output behavior model mayreflect a pattern of behavior in the context of the time of day, day ofthe week, a general pattern of movements, and other items. The behaviormodel may be derived primarily from current observance and measurementdata which may show information such as times and amount of usage oflights, television, refrigerator, washer, dryer, and other devices. Oneway to determine what equipment (including power systems equipmentand/or end use equipment) is running in a facility may be by seeing howit reacts to system disturbances (e.g., power system disturbances).Reaction of equipment, occupants and other elements, to disturbances anddisruptiveness may also be detected, analyzed and modeled. Thedisturbances may be of normal occurrence or purposively introduced intothe power supplied to the facility, space or customer system. Forexample, if there is a low voltage condition, capacitor bank switching,or other power system change or disturbance, the customer systemreaction may tell one a great deal about what is installed in terms ofonsite generation, power instrumentation, equipment (e.g., motors, andso forth), and about occupants, people and other elements.

FIG. 4 is an illustrative block diagram of an example for modeling froman element, such as a space and/or occupant, so as to infer informationabout the element which may be referred to as a customer of power usage.Obtained data and information at symbol 53 may be obtained about theelement and processes into a data model at symbol 54. The data model maybe compared and/or matched at symbol 55 with pre-defined models 56 froma database 57. The predefined models may be many types of models at thecomponent or analytic model, where the component or aspect to be modeledcould be a building, the power system itself, specific equipment orsystems, or human behavior and the analytic model may includestatistical models, power system models (physics-based), buildingenvelope models, and neural networks, to name a few. The result of thecomparison or matching may be one or more pre-defined model matches ornear matches at symbol 58. From such a match or near match, informationassociated with the match or near match may be inferred at symbol 59about the element. New models might be created over time, either fromwithin the system (as in when a new piece of equipment is identified) orby an external user or the remote site.

The models may include power system models, equipment models, individualhuman behavior models and collections of these. Depending upon theapplication, this may be very simple—where one is looking at aparticular piece of equipment and its condition over time. Or it may bemore hierarchical in nature, where one is looking at a person'sbehavior, so one may first build up the equipment model and systemmodel, then each of the human behaviors and finally the collection ofthose behaviors.

FIGS. 5 and 6 are diagrams of an illustrative example of the presentsystem. The example may use current as a basis for non-intrusivelyobtaining information about an element. Current at a certain voltage(i.e., power) may come from a substation or distribution transformer 11and be provided to a customer's premise 12 via a power line 16. Also,there may be a site transformer 13 along power line 16 located on or offthe premise 12. A sensor and processing arrangement 14 of FIG. 5 may becoupled to a power line 16 before the site transformer 13 or anarrangement 15 of FIG. 6 may be connected to power line 16 between sitetransformer 13 and the premise 12, particularly if the site transformer13 is not on the customer's premises. The customer's premises may be asingle residential home, a commercial building, a campus, an industrialfacility, micro grid, or other place. Often, just one arrangement 14 or15 would be used even though both could be used in one configuration.

Sensor and processing arrangement 14 may incorporate a sensor module 17coupled to the power line 16, a processing module 18 connected to sensormodule 17, and a processing and storage module 20 connected toprocessing module 18 via a line 39. Modules 17 and 18 may be together inan acquisition unit 28. An analysis and modeling module 19 may beconnected to the processing and storage module 20. Modules 19 and 20 maybe together in a processor unit 29. Conveyance of signals betweenacquisition unit 28 and processor unit 29 may be by cable, RF, or otherapproaches, as represented by line 39. A results module 37 may beconnected to the analysis and modeling module 19. From results module 37may be information provided to a user interface 38.

Arrangement 15 may similarly incorporate a sensor module 17, aprocessing module 18 connected to sensor module 17, a processing andstorage module 20 connected to processing module 18, and an analysis andmodeling module 19 connected to processing and storage module 20. Aresults module 37 may be connected to the analysis and modeling module19. From results module 37 may be information provided to a userinterface 38. Other “external” data that might be used could either beentered from the user interface 38 and into the processing and storagemodule 20 (accessed remotely), or could be gathered and sent to theprocessing and storage module 20. The “unit” boxes may be either localor remote to the line being measured, and may be a combination of localand remote unit boxes with some processing/storage and analysisoccurring locally for high frequency and harmonic data and some remotely(to protect the data at a central area).

Sensor module 17 may have one or more sensors. Processing module 18 maybe local to the sensor module 17. Modules 17 and 18 may be together inan acquisition unit 28. Processing and storage module 20 may be remotelylocated from transformer 11 or 13 along with the analysis and modelingmodule 19. Module 18 may be a preprocessor of sensor signals forconveyance along a line 39 to the processing and storage module 20.Conveyance of signals between units 28 and 29 may be by cable, RF, orother approaches, as represented by line 39. Arrangements 14 and 15 maybe similar to each other but different relative to placement. Eitherarrangement 14 or 15 may be used alone, or both arrangements, undercertain circumstances, may be used relative to non-intrusive monitoringof premise 12.

To recap, there may be a remote non-intrusive monitor of a space foroccupants, having a non-intrusive sensor module for monitoring a space,and a processor connected to the sensor module. The sensor module mayprovide signals representative of electrical power used at the space.The signals representative of the electrical power may be analyzed bythe processor. Analysis by the processor of the signals may result ininformation about a space and/or an element, component, environment,and/or occupant, if any, of the space.

If the electrical power used at the space contains disturbances fromwithin or outside the space which are indicated in the signalsrepresentative of the electrical power, analysis by the processor of thedisturbances may result in information about the space, element,component, environment, and/or an occupant, if any, of the space. Theinformation about the space and/or an occupant, if any, of the space,may be a result of analysis by the processor of the one or more itemsselected from a group consisting of frequency, waveform, amplitude,wavelength and harmonics, in the signals representative of theelectrical power.

Information about the space, element, component, environment, and/or anoccupant, if any, may be a result of analysis by the processor of one ormore items from a group consisting of frequency, waveform, amplitude,wavelength and harmonics, in signals representative of the electricalpower containing a reaction to disturbances in the signalsrepresentative of the electrical power.

One or more items versus time may be entered in a plot. The plot is abasis for information about an element, component, equipment,environment, and/or occupant. One or more certain patterns of the plotmay be correlated with one or more pre-determined patterns representingspecific behavior of the element, component, equipment, environment,and/or occupant.

If one or more certain patterns of the plot are correlated with the oneor more pre-determined patterns representing specific behavior of theelement, component, equipment, environment, and/or occupant, then one ormore certain patterns of the plot may be indicative of the element,component, equipment, environment, and/or occupant, if any, having thecertain behavior. The certain behavior of equipment, if any, mayincorporate ramping on/off of furnaces, chiller performance,refrigerator usage, stove operation, inverters, motors, lightingactivity, and/or other equipment operation at the space.

The one or more items versus time may be entered in a plot of amplitudeversus time. The plot may be a basis for one or more models of a space.The one or more models may be updated periodically and/or iterativelywith information derived from analysis of the signals from the sensormodule. Virtually all models may be updated periodically and/oriteratively with information derived from analysis of the signals from asensor module or other detection approach, analysis input and/or humaninput. Information about the element, component, environment, and/or anoccupant, if any, of the space, may be obtained from an internet orother information source, such as other sensed data.

There may be an approach for non-intrusive monitoring of an element,incorporating non-intrusive obtaining data associated with a sensor orother detection approach of an element, processing the data into atleast one model of the element, matching the model to one or morepredefined models representing certain information, associating thecertain information with the element from the matching of the model ofthe element with the one or more predefined models representing thecertain information, and creating improved models based upon learningand training from disturbances, human input, previously improved models,and so forth. The approach may also incorporate non-intrusive obtainingadditional data associated the element. The model of the element maylearn the additional data and modify itself in response to theadditional data. The element may be a utility power system. The one ormore predefined models may incorporate models of electricalcharacteristics, mechanical characteristics, performance signatures,environmental affects, regression, and/or predictive aspects of theutility power system.

The approach may also incorporate taking user input and modifying thedata processed into the model of the element in response to the userinput. The element may be a utility power system for providingelectricity to a building. The model of the element may be updatedthrough real-time analysis of a response of the building to utilitypower system disturbances on a line between the utility power system andthe building.

The approach may also incorporate creating a utility power system modelas an electricity provider to one or more buildings, creating a userpower system model of an electricity consumer at a connected side to theone or more buildings, modifying the utility power system model overtime from data, and modifying the user power system model modified overtime from data.

The element may be the electricity consumer. The user power system modelmay be modeled as a single block. The single block may be broken outinto load blocks, and each load block may be equipment based on asignature of electricity use by the electricity consumer. Each loadblock based on the signature may be combined with a behavior analysis ofeach load block based on disturbances from the utility power system.Each load block based on the signature may be combined with ambientconditions. Human behavior may be identified based on characteristics ofa load block.

If human behavior is a core need for non-intrusive obtaining dataassociated with an element, data from use of gas, water, telephone, andso forth, may be processed to create, update, and/or modify the model ofthe element and to make decisions about the human behavior.

Data may additionally be associated with an item associated with theelement. The data may be selected from a group comprising electricalpower usage, gas usage, sound emanation, movement of the element,movement of an item associated with the element, images of the element,images of an item associated with the element, lighting of the element,lighting of an item associated with the element, RF signals from an areaproximate to the element, RF signals from an area proximate to an itemassociated with the element, and/or publicly available information aboutthe element and an item associated with the element.

There may be a non-intrusive monitor system having a non-intrusiveinformation acquisition mechanism, an analysis module connected to thenon-intrusive acquisition mechanism, a modeling module connected to theanalysis module, and a resultant module connected to the modelingmodule. Information may incorporate one or more items relevant to one ormore elements. The modeling module may be for matching a model to anelement based on one or more items relevant to the element. The modelmay imply other information about the element.

The element may be or have a space, person or persons, equipment,environment, behavior, system, and/or system of systems, and/or anycombination of two or more of the space, person or persons, equipment,environment, behavior, system, and system of systems.

The system may also have a user interface connected to the resultantmodule and to the non-intrusive information acquisition mechanism. Theuser interface may control acquisition and collection of information,and/or receipt of selected information.

The non-intrusive information acquisition mechanism may acquire andcollect information about the one or more elements. The information mayincorporate one or more items of a group having waveforms of currentand/or voltage of electrical power to an element, images of an element,sound signals from an element, RF signals from an area proximate to anelement, background information about an element, and reactions of anelement to one or more other items of the group.

In the present specification, some of the matter may be of ahypothetical or prophetic nature although stated in another manner ortense.

Although the present system has been described with respect to at leastone illustrative example, many variations and modifications will becomeapparent to those skilled in the art upon reading the specification. Itis therefore the intention that the appended claims be interpreted asbroadly as possible in view of the prior art to include all suchvariations and modifications.

What is claimed is:
 1. A non-intrusive monitor of a space for occupants,comprising: a sensor module for monitoring a space; and a processorconnected to the sensor module; and wherein: the sensor module providessignals representative of data related to the space; the processorobtains utility information from a utility company, the utilityinformation is related to energy usage of an area including the space;and the processor analyzes the signals representative of data sensed atthe space and the utility information to model a predicted energy usageof the space.
 2. The monitor of claim 1, wherein: if electrical powerused at the space contains disturbances from within or outside the spacewhich are indicated in the signals representative of data sensed at thespace, analysis by the processor of disturbances results in informationabout the space, element, component, environment, and/or an occupant, ifany, of the space; and the information about the space and/or anoccupant, if any, of the space, is a result of analysis by the processorof one or more items selected from a group consisting of frequency,waveform, amplitude, wavelength and harmonics, in the signalsrepresentative of the electrical power.
 3. The monitor of claim 2,wherein information about the space, element, component, environment,and/or an occupant, if any, is a result of analysis by the processor ofone or more items from a group consisting of frequency, waveform,amplitude, wavelength and harmonics, in signals representative of theelectrical power containing a reaction to disturbances in the signalsrepresentative of the electrical power.
 4. The monitor of claim 2,wherein: the one or more items versus time are entered in a plot; theplot is a basis for information about an element, component, equipment,environment, and/or occupant; and one or more certain patterns of theplot correlate with one or more pre-determined patterns representingspecific behavior of the element, component, equipment, environment,and/or occupant.
 5. The monitor of claim 4, wherein: if one or morecertain patterns of the plot, correlate with the one or morepre-determined patterns representing specific behavior of the element,component, equipment, environment, and/or occupant, then one or morecertain patterns of the plot are indicative of the element, component,equipment, environment, and/or occupant, if any, having the specificbehavior; and the specific behavior of equipment, if any, compriseramping on/off of furnaces, chiller performance, refrigerator usage,stove operation, inverters, motors, lighting activity, and/or otherequipment operation at the space.
 6. The monitor of claim 2, wherein:the one or more items versus time are entered in a plot of amplitudeversus time; the plot is a basis for one or more models of a space; andthe one or more models are updated periodically and/or iteratively withinformation derived from analysis of the signals from the sensor module.7. The monitor of claim 6, wherein virtually all models are updatedperiodically and/or iteratively with information derived from analysisof the signals from a sensor module or other detection approach,analysis input and/or human input.
 8. The monitor of claim 1, wherein atleast some of the data related to the space is obtained from an internetor other information source, such as other sensed data.
 9. The monitorof claim 1, wherein: the sensor module is a non-intrusive sensor modulefor monitoring an occupancy of the space; and the sensor module providessignals representative of occupancy of the space to the processor. 10.The monitor of claim 1, wherein the processor analyzes the signalsrepresentative of the data related to the space and the utilityinformation to determine an energy usage model that is enhanced overtime.
 11. A method for non-intrusive monitoring of an element,comprising: non-intrusively obtaining data associated with a sensor orother detection approach of an element; obtaining utility informationfrom a utility company concerning energy usage for an area including theelement; processing the data and utility information to model an energyusage of the element; creating improved models to improve the energyusage of the element based upon one or more of learning and trainingfrom disturbances, user input, previously improved models, and so forth.12. The method of claim 11, further comprising: non-intrusivelyobtaining additional data associated the element; and wherein the modelof an energy usage of the element learns the additional data andmodifies itself in response to the additional data.
 13. The method ofclaim 11, further comprising taking user input and modifying the dataprocessed into the model of the energy usage of the element in responseto the user input.
 14. The method of claim 11, further comprises:creating a utility power system model as an electricity provider to oneor more buildings; creating a user power system model of an electricityconsumer at a connected side to the one or more buildings; modifying theutility power system model over time from data; and modifying the userpower system model modified over time from data; and wherein: theelement is the electricity consumer; the user power system model ismodeled as a single block; the single block is broken out into loadblocks; each load block is equipment based on a signature of electricityuse by the electricity consumer; each load block based on the signatureis combined with a behavior analysis of each load block based ondisturbances from the utility power system; each load block based on thesignature is combined with ambient conditions; and human behavior isidentified based on characteristics of a load block.
 15. The method ofclaim 14, wherein if human behavior is a core need for obtaining dataassociated with an element, data from use of gas, water, telephone, andso forth, are processed to create, update, and/or modify the model ofthe element and to make decisions about the human behavior.
 16. Themonitor of claim 11, wherein: the data can additionally be associatedwith an item associated with the element; and the data are selected froma group comprising electrical power usage, gas usage, sound emanation,movement of the element, movement of an item associated with theelement, images of the element, images of an item associated with theelement, lighting of the element, lighting of an item associated withthe element, RF signals from an area proximate to the element, RFsignals from an area proximate to an item associated with the element,and/or publicly available information about the element and an itemassociated with the element.
 17. A non-intrusive monitor systemcomprising: an information acquisition mechanism; an analysis moduleconnected to the information acquisition mechanism; a modeling moduleconnected to the analysis module; and a resultant module connected tothe modeling module; and wherein: information comprises one or moreitems relevant to one or more elements and utility information from autility company, the utility information is for an area including theone or more elements; the modeling module determines a predicted energyusage model for the one or more elements based on the one or more itemsrelevant to the one or more elements and the utility information. 18.The system of claim 17, wherein the one or more elements comprise aspace, person or persons, equipment, environment, behavior, system,and/or system of systems, and/or any combination of two or more of thespace, person or persons, equipment, environment, behavior, system, andsystem of systems.
 19. The system of claim 17, further comprising: auser interface connected to the resultant module and to the informationacquisition mechanism; and wherein the user interface controlsacquisition and collection of information, and/or receipt of selectedinformation.
 20. The system of claim 18, wherein: the informationacquisition mechanism acquires and collects at least some of theinformation relevant to the one or more elements; and the informationcomprises one or more items of a group comprising: waveforms of currentand/or voltage of electrical power to an element; occupancy of anelement; images of an element; sound signals from an element; RF signalsfrom an area proximate to an element; background information about anelement; and reactions of an element to one or more other items of thegroup.